{"type":"ScholarlyArticle","title":"CoupledCF: Learning Explicit and Implicit User-item Couplings in Recommendation for Deep Collaborative Filtering","authors":[{"name":"Quangui Zhang","slug":"quangui-zhang","given":"Quangui","family":"Zhang","affiliation":"Liaoning Technical University"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"University of Technology Sydney"},{"name":"Chengzhang Zhu","slug":"chengzhang-zhu","given":"Chengzhang","family":"Zhu","affiliation":"University of Technology Sydney"},{"name":"Zhiqiang Li","slug":"zhiqiang-li","given":"Zhiqiang","family":"Li","affiliation":"Liaoning Technical University"},{"name":"Jinguang Sun","slug":"jinguang-sun","given":"Jinguang","family":"Sun","affiliation":"Liaoning Technical University"}],"abstract":"Non-IID recommender system discloses the nature of recommendation and has shown its potential in improving recommendation quality and addressing issues such as sparsity and cold start. It leverages existing work that usually treats users/items as in- dependent while ignoring the rich couplings within and between users and items, leading to limited performance improvement. In reality, users/items are related with various couplings existing within and between users and items, which may better ex- plain how and why a user has personalized pref- erence on an item. This work builds on non- IID learning to propose a neural user-item cou- pling learning for collaborative filtering, called CoupledCF. CoupledCF jointly learns explicit and implicit couplings within/between users and items w.r.t. user/item attributes and deep features for deep CF recommendation. Empirical results on two real-world large datasets show that CoupledCF significantly outperforms two latest neural recom- menders: neural matrix factorization and Google’s Wide&Deep network.","doi":"10.24963/ijcai.2018/509","url":"https://ijcai.org/proceedings/2018/509","pdf":"https://ijcai.org/proceedings/2018/0509.pdf","markdown":"https://ijcai.org/proceedings/2018/509.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/509","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":509,"pages":{"first":3662,"last":3668},"keywords":["Machine Learning: Recommender Systems","Machine Learning: Learning Preferences or Rankings"],"videos":[],"references":[{"position":1,"raw":"[Bayer et al., 2017] Immanuel Bayer, Xiangnan He, Bhargav Kanagal, and Steffen Rendle. 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